The present disclosure generally relates to the field of computing. More particularly, an embodiment of the invention relates to distributing intelligence across networks.
Introduction of faster Internet service has enabled some end-users to access data at speeds and bandwidths that rival or exceed the traditional T-carrier 1 digital transmission line (T-1) connections. Most on-demand services, however, rely on buffering of data. That is, the corresponding data is downloaded and stored for future access. For example, to watch a movie, a user may have to download a movie first. The buffering is generally required because bandwidth over broadband connections may not be guaranteed to ensure a satisfactory quality of service (QoS) outcome. Also, keeping the buffered data secure may not be an easy task, possibly resulting in data security vulnerabilities, in part, because the content is stored locally on an end-user's computer and more prone to unauthorized access.
For example, some traditional network architectures may take advantage of statistical multiplexing of subscribers. More particularly, content services may be processed at a remote centralized content processing node and then pushed across a best effort network. For some Internet protocol (IP) services, this deployment model results in time-shifted content to be delayed, dropped, and retransmitted. As more services and content are added, this model will bottleneck and cause congestion at the edge of the network, for example, causing dropped packets and unacceptable jitter. Accordingly, such best effort models will not be adequate for latency sensitive content and functions, e.g., because there is no guaranteed QOS with such approaches.